AI-Driven Path Planning for Planetary Exploration Rovers

Abstract

This paper presents an AI-driven path planning approach for planetary exploration rovers, enabling autonomous navigation in complex and dynamic environments. We propose a framework that integrates machine learning algorithms with sensor data to enhance the rovers' ability to navigate challenging terrains. The study focuses on developing robust path planning algorithms that account for environmental variability, obstacles, and mission objectives. Experimental results validate the effectiveness of the proposed approach, demonstrating improved navigation efficiency and safety for planetary rovers. This research contributes to the advancement of autonomous systems for planetary exploration, paving the way for future missions on Mars and other celestial bodies.